Summary of Less Is More: Pre-training Cross-lingual Small-scale Language Models with Cognitively-plausible Curriculum Learning Strategies, by Suchir Salhan et al.
Less is More: Pre-Training Cross-Lingual Small-Scale Language Models with Cognitively-Plausible Curriculum Learning Strategies
by Suchir Salhan, Richard Diehl Martinez, Zébulon Goriely, Paula Buttery
First submitted to arxiv on: 30 Oct 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Curriculum Learning has been a prominent strategy to enhance the cognitive plausibility of Small-Scale Language Models (SSLMs) in the BabyLM Challenge. However, it has not led to significant improvements over non-curriculum models. This study explores whether theoretical linguistic acquisition theories can be used to specify more fine-grained curriculum learning strategies for SSLMs, creating age-ordered corpora of Child-Directed Speech for four typologically distant language families. By implementing SSLMs and acquisition-inspired curricula cross-lingually, we find that three objective curricula (Growing, Inwards, and MMM) can outperform non-curriculum baselines and derive performance benefits from fine-grained language-specific curricula that precisely replicate language acquisition theories. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper tries to make small language models better by using a special way of learning called Curriculum Learning. They test this method on four different languages and find that it can work really well if done just right. The idea is to create a curriculum that helps the model learn like a child does when they first start speaking. By doing this, they hope to make language models more realistic and useful for things like chatbots or translation software. |
Keywords
» Artificial intelligence » Curriculum learning » Translation
